
import numpy as np



class    _snake_case :


 def __init__(       self):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   (0, 0)
   lowercase__	: Optional[int]     		=   None
   lowercase__	: Optional[int]     		=   0
   lowercase__	: Optional[Any]     		=   0
   lowercase__	: Tuple     		=   0


 def __eq__(       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return self.position == cell.position



 def   lowercase__  (       self):


   '''simple docstring'''




   print(self.position)



class    _snake_case :


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_=(5, 5)):


   '''simple docstring'''




   lowercase__	: List[Any]     		=   np.zeros(SCREAMING_SNAKE_CASE_)
   lowercase__	: int     		=   world_size[0]
   lowercase__	: Tuple     		=   world_size[1]


 def   lowercase__  (       self):


   '''simple docstring'''




   print(self.w)



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Any     		=   [
       (-1, -1),
       (-1, 0),
       (-1, 1),
       (0, -1),
       (0, 1),
       (1, -1),
       (1, 0),
       (1, 1),
   ]
   lowercase__	: int     		=   cell.position[0]
   lowercase__	: Optional[Any]     		=   cell.position[1]
   lowercase__	: Tuple     		=   []
   for n in neughbour_cord:
     lowercase__	: List[Any]     		=   current_x + n[0]
     lowercase__	: List[str]     		=   current_y + n[1]
     if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
       lowercase__	: Tuple     		=   Cell()
       lowercase__	: str     		=   (x, y)
       lowercase__	: int     		=   cell
       neighbours.append(SCREAMING_SNAKE_CASE_)
   return neighbours

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_ )		->						Tuple:


  '''simple docstring'''






  lowercase__	: List[str]     		=   []
  lowercase__	: int     		=   []
  _open.append(lowercase_ )

  while _open:
    lowercase__	: Tuple     		=   np.argmin([n.f for n in _open] )
    lowercase__	: List[str]     		=   _open[min_f]
    _closed.append(_open.pop(lowercase_ ) )
    if current == goal:
      break
    for n in world.get_neigbours(lowercase_ ):
      for c in _closed:
        if c == n:
          continue
      lowercase__	: Dict     		=   current.g + 1
      lowercase__		,     lowercase__	: Dict     		=   n.position
      lowercase__		,     lowercase__	: int     		=   goal.position
      lowercase__	: Optional[int]     		=   (ya - ya) ** 2 + (xa - xa) ** 2
      lowercase__	: List[str]     		=   n.h + n.g

      for c in _open:
        if c == n and c.f < n.f:
          continue
      _open.append(lowercase_ )
  lowercase__	: Tuple     		=   []
  while current.parent is not None:
    path.append(current.position )
    lowercase__	: Dict     		=   current.parent
  path.append(current.position )
  return path[::-1]


if __name__ == "__main__":
     lowerCamelCase__     :		Union[str, Any]        = Gridworld()
     # Start position and goal
     lowerCamelCase__     :		List[Any]        = Cell()
     lowerCamelCase__     :		Dict        = (0, 0)
     lowerCamelCase__     :		Dict        = Cell()
     lowerCamelCase__     :		str        = (4, 4)
     print(f'''path from {start.position} to {goal.position}''')
     lowerCamelCase__     :		Tuple        = astar(world, start, goal)
     #   Just for visual reasons.
     for i in s:
          lowerCamelCase__     :		Union[str, Any]        = 1
     print(world.w)








"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase        =				logging.get_logger(__name__)

UpperCAmelCase        =				{
    """shi-labs/nat-mini-in1k-224""": """https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json""",
    # See all Nat models at https://huggingface.co/models?filter=nat
}



class 			lowercase					( lowercase__   ,lowercase__		):
			lowercase        =     '''nat'''

			lowercase        =     {
			    '''num_attention_heads''': '''num_heads''',
			    '''num_hidden_layers''': '''num_layers''',
			}






			def __init__(self  :	Union[str, Any]      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]=4      ,SCREAMING_SNAKE_CASE_  :	Dict=3      ,SCREAMING_SNAKE_CASE_  :	int=64      ,SCREAMING_SNAKE_CASE_  :	Tuple=[3, 4, 6, 5]      ,SCREAMING_SNAKE_CASE_  :	Dict=[2, 4, 8, 16]      ,SCREAMING_SNAKE_CASE_  :	Optional[int]=7      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]=3.0      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]=True      ,SCREAMING_SNAKE_CASE_  :	Any=0.0      ,SCREAMING_SNAKE_CASE_  :	List[Any]=0.0      ,SCREAMING_SNAKE_CASE_  :	Dict=0.1      ,SCREAMING_SNAKE_CASE_  :	Tuple="gelu"      ,SCREAMING_SNAKE_CASE_  :	str=0.02      ,SCREAMING_SNAKE_CASE_  :	str=1e-5      ,SCREAMING_SNAKE_CASE_  :	int=0.0      ,SCREAMING_SNAKE_CASE_  :	List[Any]=None      ,SCREAMING_SNAKE_CASE_  :	Tuple=None      ,**SCREAMING_SNAKE_CASE_  :	Union[str, Any]      ,)       ->	Tuple:





				"""simple docstring"""


				super().__init__(**SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  patch_size
				lowerCAmelCase												=  num_channels
				lowerCAmelCase												=  embed_dim
				lowerCAmelCase												=  depths
				lowerCAmelCase												=  len(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  num_heads
				lowerCAmelCase												=  kernel_size
				lowerCAmelCase												=  mlp_ratio
				lowerCAmelCase												=  qkv_bias
				lowerCAmelCase												=  hidden_dropout_prob
				lowerCAmelCase												=  attention_probs_dropout_prob
				lowerCAmelCase												=  drop_path_rate
				lowerCAmelCase												=  hidden_act
				lowerCAmelCase												=  layer_norm_eps
				lowerCAmelCase												=  initializer_range
				# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
				# this indicates the channel dimension after the last stage of the model
				lowerCAmelCase												=  int(embed_dim * 2 ** (len(SCREAMING_SNAKE_CASE_			) - 1)			)
				lowerCAmelCase												=  layer_scale_init_value
				lowerCAmelCase												=  ['''stem'''] + [F"""stage{idx}""" for idx in range(1      ,len(SCREAMING_SNAKE_CASE_			) + 1			)]
				lowerCAmelCase							,       lowerCAmelCase												=  get_aligned_output_features_output_indices(
				    out_features=SCREAMING_SNAKE_CASE_      ,out_indices=SCREAMING_SNAKE_CASE_      ,stage_names=self.stage_names			)


